Dr. Tim O’Connell on Leading AI Adoption in Healthcare Systems

Introduction: Why Leadership Matters in the Age of AI

In the swirl of artificial intelligence hype and healthcare transformation, the stakes have never been higher. Hospital leaders face pressure to innovate, sometimes at a pace that outstrips caution, while clinicians, administrators, and technologists navigate an increasingly complex and consequential landscape. AI promises operational efficiency, clinical breakthroughs, and even new hope for addressing longstanding disparities. But who keeps the system honest? Who ensures that technology is a servant, not a master, to the needs of patients and communities?

Tim O’Connell, MD, MEng, practicing radiologist and CEO of emtelligent, stands at the intersection of these challenges. His career arc, from network engineering to the highest levels of medical AI entrepreneurship, illuminates not only the promise of AI but also its potential pitfalls. As he warns, “our healthcare leaders and organizations, institutions, payers—wherever they are—need to remain the gatekeepers to ensure that implementing new technologies doesn’t happen too quickly or for the wrong reasons and that it adversely affects patient care.”

This article explores Dr. O’Connell’s unique perspective on AI in healthcare, the importance of clinical leadership, the urgent need for community collaboration, and the non-negotiable focus on patient and clinician well-being.

The Unlikely Engineer: Dr. Tim O’Connell’s Hybrid Path

Before he was Dr. O’Connell, physician and medical AI CEO, Tim O’Connell was a network engineer, designing systems for telecom giants. “I’ve got a master’s degree in engineering and I worked for both a large equipment manufacturer and a large telephone company, doing networking and working in information technology,” he explains. “So I’ve been able to combine my love of medicine with, you know, my love of technology—and every day is a great day in the candy shop.”

His dual fluency—in the language of computers and the lived experience of clinical medicine—has shaped Emtelligent’s mission: to make sense of unstructured medical text, the free-form reports generated by doctors, nurses, and care teams every day. O’Connell spends one to two days a week practicing radiology in Vancouver, British Columbia, and the rest immersed in building tools to transform how healthcare systems use data.

The core problem is one many health leaders know too well: vital information is locked in the narrative notes of patient charts, inaccessible to most analytics tools. “We started the company with some partners about eight years ago now to solve some of the problems that I certainly see in healthcare with unstructured medical text,” O’Connell says.

The Three Fronts of AI’s Impact: Efficiency, Outcomes, and Clinical Practice

AI’s Promise—And the Work Still to Do

When asked how AI is shaping the future, O’Connell’s response is pragmatic. “The landscape is moving so quickly right now, I think I can predict where we’re going to be five months from now, but ten months from now, I have no idea,” he quips, echoing Bill Gates. The frenzy is real, but so is the opportunity.

O’Connell sees three main fronts for AI in healthcare:

1. Operational Efficiency “Compared to a lot of other organizations like manufacturing, I think in healthcare, we have relatively poor organizational efficiency. A lot of our workflows are very manual,” O’Connell notes. Automating routine handoffs, monitoring patient outcomes to prevent issues like hospital-acquired infections, and using AI for administrative tasks are all within reach.
2. Clinical Outcomes and Safety Beyond admin, AI’s greatest potential may be in catching complex diagnoses that challenge even experienced clinicians. “There was a really interesting study…where a large language model was able to do a better job diagnosing complex patient disorders than humans were—and was even better than human and large language model alone,” he recounts.
3. Enabling Precision Medicine As medicine moves toward individualized care, AI tools could one day help clinicians synthesize data from genetics, lifestyle, and social determinants to personalize treatment.

But O’Connell is clear-eyed about the obstacles.

Barriers to Adoption: Beyond the Hype

What’s Holding AI Back?

Despite rapid progress, O’Connell identifies four major barriers to meaningful AI adoption in healthcare:

• Product Reality vs. Promise: “The products have to actually work…You may find that the accuracy is unacceptable,” he warns, describing how some generative models fall short when deployed at scale.
• Complex Organizational Change: “You can’t go into an organization as complex as a hospital, where if you change one thing, there’s all these downstream effects—that butterfly flapping its wings in Brazil kind of effect,” O’Connell explains. New workflows can have unpredictable ripple effects on staff and patient safety.
• Security and Data Trust: “Some of the hyperscalers can be fairly opaque about how they plan to use your data. And, you know, ‘we’re retaining your data for 30 days, but we’re not going to use it’—and what does that mean?” He urges healthcare organizations to demand more clarity and control over sensitive patient data.
• Return on Investment (ROI): AI can be a costly “party trick” if not carefully matched to true clinical or business value. “There may be a massive implementation cost or an opportunity cost…and at the end of the day, it doesn’t actually save you money or produce efficiency.”

In short: robust pilots, real-world evidence, and transparency are essential before full-scale adoption.

Building Trust: Leadership and the Clinician Voice

The Human Factor—Why Trust Matters More Than Tech

If AI is to fulfill its potential, it needs more than data and algorithms; it needs trust from clinicians, administrators, and patients.

O’Connell pushes back against the stereotype that clinicians fear AI as a job threat. “I really don’t think there are any doctors out there who are like, ‘Oh, I don’t want to use this thing because I’m worried about it stealing my job.’ It’s more that people have used AI products or workflow augmentation products in the past and had bad experiences.”

So what does build trust?

• Rigorously Run Pilots and Proof of Concept: “We’re all about rigor around experimental testing. There has to be ethics review…establishing non-inferiority is often the goal. We need to approach implementing changes in workflow the exact same way.”
• Transparency in Leadership: Leadership must avoid conflicts of interest and be transparent in decision-making. O’Connell draws a vivid analogy: “People have made accusations in the past about people in the military—that by the time they get to be a general, they’re really just auditioning for a job with a defense contractor. And I think the same thing is true in healthcare leadership…Why did you do that? Were you auditioning for a job with them? So we need to be very transparent about potential conflicts.”

The lesson: AI is not a silver bullet. Its success hinges on inclusive leadership, honest communication, and a relentless focus on patient and clinician needs.

Leadership’s Mandate: The Hierarchy of Needs in Technology Implementation

Aligning Technology With Core Values

How should physician executives and clinical leaders champion AI initiatives without losing sight of what matters? O’Connell borrows from psychology to make his point: “People are familiar with Maslow’s hierarchy of needs, right? For bottom line, we need sort of shelter and food and things like that. There’s absolutely a hierarchy of needs in healthcare and in project implementation, and the number one hierarchy is a patient always comes first. Absolutely. There can be no negative impact on patient care.”

This hierarchy, he argues, should guide every decision:

1. Patient First: Any new workflow or technology must improve, or at least do no harm to, patient care.
2. Sustainability of Care: Does this solution make clinicians’ jobs harder or increase burnout? If so, it may cause more harm than good. “If you create an extra hour of work for one of the caregivers involved in the patient workload…this may result in people quitting jobs or changing jobs, and so this is going to be a net negative impact.”
3. Organizational Health: Only after patient and staff needs are met should leaders evaluate ROI, profit, or efficiency.

This ethical sequence echoes through O’Connell’s philosophy and underscores the essential role of clinical leaders in organizational decision-making.

Tackling Disparities: Can AI Bridge the Equity Gap?

Personalized Medicine and Social Justice

The conversation inevitably turns to personalized medicine and health equity—a topic that sits at the heart of modern healthcare debates.

Dr. O’Connell is refreshingly candid: “I think it’s one of the most pressing questions of our time in healthcare—how can we reduce disparities in health care delivery? The very short answer is: I’m not exactly sure.”

He notes that before technology can solve equity, healthcare systems must first build trust with marginalized communities. “I’ve met many patients in marginalized communities, and they don’t even want to seek care because they’ve had such negative experiences in the past…I think we have some fundamental problems with our health care systems and our health care delivery models that need to be addressed first.”

That said, he sees promise in using AI for more representative training data and awareness of rare conditions. He shares a telling story: “I just had a clinical case where I looked at a chest x-ray. I’m based in Vancouver; we have a very small population of African American individuals here. I called the emergency room doctor and I said, ‘I think this very young patient has sickle cell anemia.’ He said, ‘How can you tell that? Why would you even think about that?’ I said, ‘Well, I trained in the U.S. Northeast, and there’s some signs here, and in my training that’s a very common disease.’ Here in Vancouver, it’s extremely uncommon. And so there’s just general unawareness of it. That emergency room doctor was very thankful, and worked the patient up, and they were positive.”

His point: clinical expertise is still essential, but AI—properly designed and trained—can help fill gaps, particularly when local training data is sparse.

Managing Change: Benchmarking and Staying Ahead

Surviving—and Thriving—Amid Rapid Change

Rapid AI advances can be exhilarating and anxiety-inducing in equal measure. For health leaders, the need to keep up can feel overwhelming.

O’Connell offers a practical strategy: “One of the ways we are able to very rapidly use new technologies and yet still maintain that focus is through good, high-quality benchmarks…data sets that have been human-annotated as a gold standard and then testing new technologies against them as they come out.”

But he also warns against overreliance on superficial metrics. “There was a movie called Catch Me If You Can…he passes the Louisiana State Bar Exam, but is he a lawyer? And you have the same question of, well, you passed your USMLE Step 2 medical licensing exam. Does that make you a doctor? And the answer is, of course, no…We’re seeing, you know, some very low quality benchmarks being used to test models, and then we’re seeing press releases: ‘Oh, this model passed the USMLE Step 2.’ And it’s like, that’s not what makes someone a physician.”

The message: demand better evidence, keep the focus on patient outcomes, and don’t be distracted by surface-level achievements.

Leadership, Wellness, and Community: The Intersections

Leading for Both Patients and Providers

Throughout O’Connell’s narrative, a recurring theme is the indivisibility of patient outcomes and clinician well-being. New technologies, if poorly implemented, risk not only patient safety but also staff morale, retention, and engagement.

He is adamant that the well-being of clinicians and the health of the community are inseparable: “If you’re going to do a whole lot of work to implement new systems and workflows, then there actually should be a net benefit to patient care—either an improvement in quality of care and safety of care or efficiency of care.”

Community collaboration is likewise non-negotiable. Tackling disparities, creating trustworthy AI, and managing disruptive change all require dialogue between clinicians, administrators, patients, technologists, and the communities they serve.

Key Takeaways: Lessons for Leaders in an AI-Enabled Healthcare Era

Practical Guidance for Clinical and Administrative Leaders

• Clinician Gatekeepers: Stay grounded. As O’Connell puts it: “Our healthcare leaders…need to remain the gatekeepers to ensure that implementing new technologies doesn’t happen too quickly or for the wrong reasons and that it adversely affects patient care.” Trust your expertise and insist on clinical oversight in all tech decisions.
• Start with Rigorous Pilots: Don’t buy into the hype. Insist on well-structured pilots and independent evaluation of new tools. Focus on real-world results, not marketing promises.
• Prioritize Transparency and Ethics: Scrutinize vendor relationships for conflicts of interest. Communicate openly with clinicians and staff about the rationale for technology choices.
• Balance Efficiency and Well-Being: Look beyond cost savings and ROI. Every change must benefit both patients and those providing care.
• Engage Communities: Technology alone won’t fix healthcare disparities. Listen to marginalized groups, and design solutions that build trust from the ground up.
• Demand Better Benchmarks: Push for gold-standard validation and don’t accept superficial metrics as proof of effectiveness.
• Be Adaptable—But Don’t Be Rushed: AI will keep evolving. Leaders must be nimble, but they must also ensure change is purposeful and safe.

Conclusion: The Human Future of Healthcare

Artificial intelligence may be the most disruptive force in modern medicine, but it will not—cannot—replace the human judgment and leadership that safeguard patients and communities. As Dr. Timothy O’Connell’s story shows, the future belongs to leaders who blend technical vision with clinical wisdom, ethical courage, and a deep commitment to both patient and provider well-being.

In an era where change is the only constant, O’Connell’s message to his peers resonates: “You shouldn’t feel bad if you find that things are moving too quickly…it’s moving quickly for everyone. But…you have to be the gatekeeper.”

Healthcare’s greatest advances will not come from algorithms alone, but from the collaborative spirit of leaders who hold technology—and themselves—to the highest standard.